Visceral fat measurement

ABSTRACT

Dual-energy absorptiometry is used to estimate visceral fat metrics and display results, preferably as related to normative data. The process involves deriving x-ray measurements for respective pixel positions related to a two-dimensional projection image of a body slice containing visceral fat as well as subcutaneous fat, at least some of the measurements being dual-energy x-ray measurements, processing the measurements to derive estimates of metrics related to the visceral fat in the slice, and using the resulting estimates. Processing the measurements includes an algorithm which places boundaries of regions, e.g., a large “abdominal” region and a smaller “abdominal cavity” region. Two boundaries of the “abdominal cavity” region are placed at positions associated with the left and right innermost extent of the abdominal muscle wall by identifying inflection of % Fat values. The regions are combined in an equation that is highly correlated with VAT measured by quantitative computed tomography in order to estimate VAT.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/730,051, titled ESTIMATING VISCERAL FAT BY DUAL-ENERGY X-RAYABSORPTIOMETRY, filed Mar. 23, 2010, which is incorporated by reference.

BACKGROUND OF THE INVENTION

Obesity can be generally predictive of morbidities such as coronaryartery disease and diabetes, and the anatomical distribution of adiposetissue (fat) can be a strong independent predictor of these and othermedical conditions and outcomes. For example, overweight subjects with alarger proportion of fat stored as visceral adipose tissue (VAT) arebelieved to be at a higher risk than similarly overweight individualswith a larger percentage of fat stored as subcutaneous adipose tissue(SAT). Studies have shown that VAT levels are a predictor ofcardiovascular risk factors, e.g. HDL, LDL, triglyceride levels, andhypertension. Because of the predictive and other values of visceral fatas distinguished from general obesity and subcutaneous fat, it isbelieved desirable to find a way to efficiently and effectively measureor estimate VAT.

It is known in the art to measure or estimate VAT by differentiating itfrom SAT in abdominal cross-sections or slices using computerizedtomography (CT) and magnetic resonance imaging (MRI). Measurements canbe made at the level of the umbilicus, where SAT and VAT volumestypically are identified by an image thresholding algorithm. However,the relatively high cost of both examinations and the high radiationdosage of CT can discourage the use of these techniques as a screeningtool for VAT levels. Further, the thresholding method lacks specificitybecause areas or volumes above the threshold can have different amountsof % fat, and areas or volumes below the threshold may not be fat-free.Thus, systematic errors can be introduced by assumptions of % fat inareas or volumes above or below the threshold.

Dual-energy x-ray absorptiometry (DXA) exams are widely available,rapid, relatively low dose, and much less costly than CT and MRI exams.Further, DXA is capable of measuring both global and regional fat massbecause, for tissue paths that are projected as pixels in the x-rayimage, a given dual-energy x-ray measurements pertains to a uniquecombination of fat and lean mass. However, the ability of DXA todistinguish between VAT and SAT has been limited because DXA is atwo-dimensional projection technique.

SUMMARY OF THE INVENTION

In accordance with one non-limiting aspect of the invention a methodcomprises acquiring x-ray measurements for respective pixel positionsrelated to a two-dimensional projection image of a portion of asubject's abdomen, wherein at least some of the measurements aredual-energy x-ray measurements; placing a plurality of regions of theimage; computer processing to combine the plurality of regions toprovide an estimate of visceral fat; and providing and displayingselected results related to said estimate of visceral.

In accordance with another non-limiting aspect of the invention a methodcomprises: acquiring x-ray measurements for respective pixel positionsrelated to a two-dimensional projection image of a portion of asubject's abdomen, wherein at least some of the measurements aredual-energy x-ray measurements; placing a first region of the imagewhich extends from a first side of the abdomen to a second side of theabdomen; placing a second region which extends across an inner abdominalcavity wall from the first side to the second side between innermostextents of an abdominal muscle wall; computer processing the first andsecond regions to provide an estimate of visceral fat; and providing anddisplaying selected results related to said estimate of visceral fat.

In accordance with another non-limiting aspect of the invention anapparatus comprises a data acquisition unit including a scanner thatacquires x-ray measurements for respective pixel positions related to atwo-dimensional projection image of a portion of a subject's abdomen,wherein at least some of the measurements are dual-energy x-raymeasurements; a memory in which is placed a plurality of regions of theimage; a processing unit that computer-processes the regions to providean estimate of visceral fat; and a display unit that provides anddisplays selected results related to visceral fat of the subject.

In accordance with another non-limiting aspect of the invention anapparatus comprises: a data acquisition unit including a scanner thatacquires x-ray measurements for respective pixel positions related to atwo-dimensional projection image of a portion of a subject's abdomen,wherein at least some of the measurements are dual-energy x-raymeasurements; a memory in which is placed a first region of the imagewhich extends from a first side of the abdomen to a second side of theabdomen, and a second region which extends across an inner abdominalcavity wall from the first side to the second side between innermostextents of an abdominal muscle wall; a processing unit thatcomputer-processes the first and second regions to provide an estimateof visceral fat; and a display unit that provides and displays selectedresults related to visceral fat of the subject.

In various non-limiting alternatives one or more functions can beautomated or partially automated with computer processing. For example,the first region can be automatically placed by a software tool usingvarious anatomical landmarks and the position of an upper region ofinterest line delineating the pelvis for reference. Further, thesoftware tool may automatically place the second region based on % Fatinflection which is indicative of the innermost extent of the abdominalmuscle wall. Further, measurements of total adipose tissue in a fixedthickness region across the entire width of the subject, e.g., justabove the pelvis at the level of the 4^(th) lumbar vertebrae, can becombined with a measurement of the adipose tissue in the same thicknessregion of the abdominal cavity plus whatever subcutaneous fat is presentabove and below the cavity region using a linear equation that iscorrelated with VAT measured by quantitative computed tomography inorder to estimate VAT.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a simplified and schematic cross-sectional elevationillustrating a fan-shaped distribution of x-rays in a DXA system inwhich the visceral fat analysis described herein can be practiced.

FIG. 2 a illustrates a PA projection image of a patient taken with a DXAsystem, and FIG. 2 b is an enlarged view of the portion of the imagecorresponding to the body slice indicated by a broken line rectangle inFIG. 2 a.

FIG. 3 illustrates a cross-sectional image of a body slice.

FIG. 4 illustrates placement of a large “abdominal” region and a smaller“abdominal cavity” region.

FIG. 5 illustrates a % fat profile and inflection points used forplacement of the abdominal cavity region.

FIG. 6 is a block diagram of a DXA system useful for estimating visceraladipose tissue.

FIG. 7 is a cross-sectional image of a body slice which illustrates useof more than two regions.

DETAILED DESCRIPTION

Referring to FIG. 1, a DXA system 10 includes a patient table 12 havinga support surface 14 that can be considered horizontal and planar inthis simplified explanation and illustration which is not necessarilyaccurate in scale or geometry, and which is used here solely toillustrate and explain certain principles of operation. A human subject26 is supine on surface 14. The length of the patient is along ahorizontal longitudinal axis defined as the y-axis and the patient'sarms are spaced from each other along the x-axis. A C-arm 16 hasportions 16 a and 16 b extending below and above table 10, respectively,and is mounted in suitable structure (not shown expressly) for moving atleast parallel to the y-axis along the length of patient 26. Lowerportion 16 a of the C-arm carries an x-ray source 20 that can emitx-rays limited by an aperture 22 into a fan-shaped distribution 24conforming to a plane perpendicular to the y-axis. The energy range ofthe x-rays can be relatively wide, to allow for the known DXAdual-energy x-ray measurements, or can be filtered or generated in anarrower range to allow for single energy x-ray measurements. The x-raydistribution can be continuous within the angle thereof or can be madeup, or considered to be made up, of individual narrower beams. The x-raydistribution 24 can encompass the entire width of the patient asillustrated, or it can have a narrower angle so the entire patient canbe covered only by several passes along the y-axis and the x-raymeasurements from the several passes can be combined as is known in theart to simulate the use of a wider fan beam, as typical in currentcommercial DXA systems. Alternatively, a single, pencil-like beam ofx-rays can be used to scan selected regions of the patient's body, e.g.in a raster pattern. The x-rays impinge on x-ray detector 28, which cancomprise one or more linear arrays of individual x-ray elements 30, eachlinear array extending in the x-direction, or a continuous detectorwhere measurements for different positions along the detector can bedefined in some manner known in the art, or can be another form ofdetector of x-rays. C-arm 16 can move at least along the y-axis, or canbe maintained at any desired position along that axis. For any oneposition, or any one unit of incremental travel in the y-direction ofarm 16, detector 28 can produce one or several lines of raw x-ray data.Each line can correspond to a row of pixels in a resulting image, whichrow extends in a direction corresponding to the x-direction. Each linecorresponds to a particular position, or range of positions, of theC-arm in its movement along the y-axis and/or a particular lineardetector, and comprises a number of individual measurements, each for arespective detector element position in the line, i.e., representsattenuation that the x-rays have suffered in traveling from source 20 toa respective detector element position over a specified time interval. ADXA system takes a higher x-ray energy measurement H and a lower x-rayenergy measurement L from each detector element position, and carriesout initial processing known in the art to derive, from the raw x-raydata, a set of pixel values for a projection image. Each pixel valuecomprises a high energy value H and a low energy value L. This can beachieved by rapidly alternating the energy level of the x-rays fromsource 20 between a higher and a lower range of x-ray energies, forexample by rapidly rotating or otherwise moving a suitable filter in orout of the x-rays before they reach patient 26, or by controlling thex-ray tube output, and/or by using an x-ray detector 28 that candiscriminate between energy ranges to produce H and L measurements foreach pixel position, e.g. by having a low energy and a high energydetector element side-by-side or on top of each other for respectivepositions in the detector array. The H and L x-ray measurements for therespective pixel positions are computer-processed as known in the art toderive estimates of various parameters, including, if desired, bodycomposition (total mass, fat mass, and lean mass).

A PA projection image taken with the DXA system is illustrated in FIG. 2a. FIG. 2 b is an enlarged view of the projection image of therelatively thick slice of the body indicated by the broken linerectangle in FIG. 2 a. As suggested by FIGS. 2 a and 2 b, pixel valuesare derived from x-ray measurements for a body slice that is along thez-x plane and has a thickness (w) in the y-direction. For example,several hundred pixel values in the x-direction and a several pixelvalues in the y-direction are derived from the raw x-ray data. Typicallybut not necessarily, the body slice thickness w along the y-direction isseveral mm, e.g. 10-15 mm.

FIG. 3 illustrates an x-ray image of a section or slice parallel to az-x plane through the abdominal region of an obese patient taken with aCT system. The image shows a ring 200 (non-circular) of subcutaneousadipose tissue (SAT) and regions 202 of visceral adipose tissue (VAT).

Referring to FIG. 3 and FIG. 4, in accordance with one embodiment of theinvention % VAT is estimated with a DXA system using an empiricaltechnique. A region of interest (ROI) is placed on a DXA scan todelineate various anatomical regions, e.g. arms, trunk, legs, etc. inaccordance with the instructions in the User's Guide for the Hologic DXAscanner. After the ROI has been placed on the scan, a large “abdominal”region 302 and a smaller “abdominal cavity” region 304, both rectangularin shape and 4 scan lines (5 cm) high, are placed on the subject'sabdomen 1-2 scan lines (1.5-2.5 cm) above the top of the pelvis regionat the level of vertebral body L4. The large “abdominal” region 302 isdefined by boundaries 306, 308, 310, 312, and extends completely acrossthe abdomen from one side to the other. The smaller “abdominal cavity”region 304 is defined by boundaries 300, 301, 306, 308, centered withinthe large region, and extends across the inner abdominal cavity. Thelarge “abdominal” region can be placed by the user based on visualinspection of the image. However, in accordance with an embodiment ofthe invention the “abdominal” region is automatically placed by asoftware tool that is stored in non-transitory computer readable memoryand run by processing hardware. For example, the software tool may placethe “abdominal” region using various anatomical landmarks and theposition of the upper ROI line delineating the pelvis for reference.

Referring to FIGS. 3 through 5, the software tool may also automaticallyplace the smaller “abdominal cavity” region 304 within the larger region302. In one embodiment this is accomplished with an algorithm whichplaces boundaries based on % Fat inflection. The upper and lowerboundaries 306, 308 of the “abdominal cavity” region are superimposedover the larger region such that the upper and lower coordinates of bothregions are identical. The left and right boundaries 300, 301 of the“abdominal cavity” region are then placed by the algorithm. Inparticular, the algorithm initially operates on percent fat profile datacorresponding to a position inside the left and right boundaries 310,312 of the large abdominal region, e.g., at the point where thesubcutaneous fat layer ends, and proceeds by operating on datacorresponding to an adjacent set of pixels moving in toward the centerthe body from the left and right sides. Initially, % Fat decreasessteadily as the x-ray beam enters the abdominal muscle band 314.Typically after one or two cm (10-20 pixels) the trend of decreasing %Fat reverses as the DXA beam exits the abdominal muscle wall and entersthe inner visceral cavity. At this point the % Fat values start toincrease. This inflection point, which is indicative of the innermostextent of the abdominal muscle wall, is detected by the algorithm, e.g.,by identifying that the % Fat values of two consecutive pixels arehigher than the preceding pixel. The “abdominal cavity” regionboundaries 300, 301 are set at the inflection point.

In practice the abdominal cavity can be located easily on one side ofthe body but may be difficult to find on the other. In this case thesize and location of the cavity wall that was found can be mirrored tothe other side by taking advantage of the presence of bilateral symmetryin the DXA anterior-posterior projection of the human body.

A linear regression technique that accounts for SAT between theboundaries of the “abdominal cavity” region is used to estimate VAT. Thelarge “abdominal” region defined by boundaries 306, 308, 310, 312provides a measurement of total adipose tissue in a 5 cm wide regionacross the entire width of the subject just above the pelvis at thelevel of the 4^(th) lumbar vertebrae. The smaller “abdominal cavity”region defined by boundaries 300, 301, 306, 308 provides a measurementof the adipose tissue in the same 5 cm wide region of the abdominalcavity plus whatever subcutaneous fat is present above (at region 320)and below (at region 322) the cavity region in the two dimensional DXAprojection. Constant percent fat values at the center of the plot inFIG. 5 indicate image pixels where bone is present and percent fatcannot be directly measured. However, techniques for estimating percentfat values for the region where bone is present and percent fat cannotbe directly measured are known. The measurement (Abd. Adipose Mass) oftotal adipose tissue in a 5 cm wide region across the entire width ofthe subject just above the pelvis at the level of the 4^(th) lumbarvertebrae and the measurement (Cavity Adipose Mass) of the adiposetissue in the same 5 cm wide region of the abdominal cavity pluswhatever subcutaneous fat is present above and below the cavity regionin the two dimensional DXA projection is combined in a linear equationthat is highly correlated with VAT measured by quantitative computedtomography in order to estimate VAT as:

DXA VAT=J*Cavity Adipose Mass−K*(Abd. Adipose Mass−Cavity AdiposeMass)+b  Eq. 1

where J and K are constants that optimize the correlation between DXAVAT and VAT measured by computed tomography, and b is the intercept termof the linear equation. It should be noted that the values of J, K and bare not necessarily that same for all subjects. For example, values ofJ, K and b can be dependent upon age, gender, ethnicity, weight, height,body mass index, waist circumference, and other anthropomorphicvariables. Those skilled in the art will understand how to determinethose constants in view of this disclosure.

The results of the processes described above can be in various forms andcan be used for a variety of purposes. For example, displays ofnumerical values can be used in assessing the health, treatment options,or treatments of a patient by a health professional. As another example,such numerical values or estimates derived therefrom can be used asinputs to automated systems for similar assessment or for treatmentplanning. As yet another example, parameters related to fat metrics canbe displayed and recorded or printed as a part of an otherwise typicalreport including x-ray images and other DXA-produced information for apatient.

Estimates of visceral fat derived as discussed above can be shown in avariety of ways. They can be displayed alone, or in combination withknown or expected ranges of comparable estimates for populationsbelieved to be “normal” or “healthy,” which ranges can be matched to theestimates for a patient by some characteristic such as age, sex, and/orethnicity. The normal or healthy ranges for such characteristics can beobtained by retrospective analysis of already completed studies and/orfrom new studies to obtain the data. A VAT metric for a patient can becompared with a VAT metric for the same patient taken at a differenttime to estimate the change and/or the rate of change, for example tosee if visceral fat parameters have improved or have deteriorated oversome period of time or in relation to some treatment or regimen. Suchchanges also can be matched to expected or known or estimated ranges tosee if the change or rate of change for a patient is statisticallysignificant as distinguished from a change within the precision range ofthe estimate. The VAT estimates derived as discussed above, or metricsbased on such estimates, can be used in other ways as well. Onenon-limiting example is to produce reports similar to those produced forBMD (bone mineral density) in current commercial bone densitometry (DXA)systems but for metrics of visceral fat (VAT) rather than BMD estimates.

FIG. 6 illustrates in block diagram form a DXA system carrying out theprocesses described above for estimating VAT. The system can be one ofthe current DXA systems offered commercially by the assignee programmedto carry out the disclosed processes, using programming that a person ofordinary skill in the art can apply to a particular commerciallyavailable DXA system without undue experimentation, given the teachingsin this patent specification. The system includes a scanner 60, computerprocessing unit 62, user interface 66, and a results presentation unit64. The scanner may include an x-ray source and x-ray detector. Scanner60 also includes appropriate other components known in the art, such aspower and control units, and operates to generate dual energy or singleenergy x-ray measurements of the selected region or slice of a patient'sbody. The computer processing unit 62 includes processing hardware andnon-transitory computer readable memory for controlling scanner 60 andprocessing x-ray measurements obtained thereby in accordance with thetechniques described above under corresponding programming. A resultspresentation unit 64 displays, prints, stores, and/or sends for furtherprocessing or storage, results such as in the form of images and/orcurves and/or numeric results indicative of VAT or % VAT, or some otherparameter related to visceral fat or other parameter discussed above,including in the immediately preceding paragraph. Units 62 and 64communicate interactively with a user input unit 66. The actual physicalarrangement of system components may differ from the functionalillustration in FIG. 6.

The disclosure above is mainly in terms of SAT and VAT of humanpatients, but it should be clear that its approach is applicable inother fields as well, such as in analysis of other subjects, such aslive animals and carcasses. Finally, while a currently preferredembodiment has been described in detail above, it should be clear that avariation that may be currently known or later developed or later madepossible by advances in technology also is within the scope of theappended claims and is contemplated by and within the spirit of thedetailed disclosure.

FIG. 7 is a cross-sectional image of a body slice which illustrates analternative embodiment utilizing more than two regions. The large“abdominal” region defined by boundaries 306, 308, 310, 312 provides ameasurement of total adipose tissue in a 5 cm wide region across theentire width of the subject just above the pelvis at the level of the4^(th) lumbar vertebrae. A smaller “cavity” region which includes afirst portion defined by boundaries 300, 700, 306, 308 and a secondportion defined by boundaries 702, 301, 306, 308 provides a measurementof the adipose tissue in the same 5 cm wide region of the abdominalcavity, exclusive of the spinal region, and plus whatever subcutaneousfat is present above (at region 320) and below (at region 322) thecavity region in the two dimensional DXA projection. The “spinal” regiondefined by boundaries 700, 306, 702, 308 provides a measurement ofadipose tissue where bone is present and percent fat cannot be directlymeasured. A generalized linear equation for combining the measurementsof adipose tissue in order to estimate VAT with three regions can berepresented as:

DXA VAT=J*Region1+K*Region2+L*Regionb 3 +b,  Eq. 2

where J, K and L are constants that optimize the correlation between DXAVAT and VAT measured by computed tomography, and b is the intercept termof the linear equation. As in the previously described embodiment, thevalues of the constants (here J, K, and L) and intercept b are notnecessarily that same for all subjects. For example, values of J, K, Land b can be dependent upon age, gender, ethnicity, weight, height, bodymass index, waist circumference, and other anthropomorphic variables.Those skilled in the art will understand how to determine thoseconstants in view of this disclosure. Furthermore, the two region andthree region embodiments are merely exemplary, and any number of regionscould be defined and utilized to estimate VAT.

In an alternative embodiment polynomial expansion is used to estimateVAT. A generalized equation for combining the measurements of adiposetissue using polynomial expansion in order to estimate VAT can berepresented as:

DXA VAT=J1(Region1)+J2(Region1)² +J3(Region1)²+ . . .  Eq. 3

where Jn and constants associated with the polynomial expansion of theother regions (eg. K_(n) and L_(n)) optimize the correlation between DXAVAT and VAT measured by computed tomography. As in the previouslydescribed embodiment, the values of the constants are not necessarilythat same for all subjects, and can be dependent upon age, gender,ethnicity, weight, height, body mass index, waist circumference, andother anthropomorphic variables.

While the invention is described through the above exemplaryembodiments, it will be understood by those of ordinary skill in the artthat modification to and variation of the illustrated embodiments may bemade without departing from the inventive concepts herein disclosed.Moreover, while the preferred embodiments are described in connectionwith various illustrative structures, one skilled in the art willrecognize that the system may be embodied using a variety of specificstructures. Accordingly, the invention should not be viewed as limitedexcept by the scope and spirit of the appended claims.

1. A method comprising: acquiring x-ray measurements for respectivepixel positions related to a two-dimensional projection image of aportion of a subject's abdomen, wherein at least some of themeasurements are dual-energy x-ray measurements; placing a plurality ofregions of the image; computer processing to combine the plurality ofregions to provide an estimate of visceral fat; and providing anddisplaying selected results related to said estimate of visceral.
 2. Themethod of claim 1 including combining the plurality of regions in alinear equation using constants that provide correlation between DXA VATand VAT measured by computed tomography.
 3. The method of claim 1including combining the plurality of regions using polynomial expansion.4. The method of claim 1 including placing a first region of the imagewhich extends from a first side of the abdomen to a second side of theabdomen, and placing a second region which extends across an innerabdominal cavity from the first side to the second side betweeninnermost extents of an abdominal muscle wall.
 5. The method of claim 1including placing a first region of the image which extends from a firstside of the abdomen to a second side of the abdomen, placing a secondregion which extends across an inner abdominal cavity from the firstside to the second side between innermost extents of an abdominal musclewall but exclusive of a third region which is placed where bone ispresent and percent fat cannot be directly measured.
 6. The method ofclaim 1 including computer processing at least some of the x-raymeasurements for placing at least one region of the image.
 7. The methodof claim 4 including using an anatomical landmark and a preselectedregion of interest line for placing the first region of the image. 8.The method of claim 4 including computer processing at least some of thex-ray measurements for placing the second region of the image.
 9. Themethod of claim 8 including identifying a left and a right innermostextent of abdominal muscle wall by identifying inflection of adiposetissue values for placing the second region of the image.
 10. The methodof claim 4 including combining the first region and the second region ina linear equation that is correlated with VAT measured by quantitativecomputed tomography for processing the first and second regions toprovide an estimate of visceral fat.
 11. The method of claim 10including calculating visceral fat as:J*second region Mass−K*(first region Mass−second region Mass)+b.
 12. Themethod of claim 11 including selecting constants J and K that providecorrelation between DXA VAT and VAT measured by computed tomography, andwherein b is an intercept term.
 13. The method of claim 11 includingselecting a value for at least one of J, K and b for the subject. 14.The method of claim 13 including selecting a value for at least one ofJ, K and b based on at least one of age, gender, ethnicity, weight,height, body mass index, waist circumference, and other anthropomorphicvariables of the subject.
 15. Apparatus comprising: a data acquisitionunit including a scanner that acquires x-ray measurements for respectivepixel positions related to a two-dimensional projection image of aportion of a subject's abdomen, wherein at least some of themeasurements are dual-energy x-ray measurements; a memory in which isplaced a plurality of regions of the image; a processing unit thatcomputer-processes the regions to provide an estimate of visceral fat;and a display unit that provides and displays selected results relatedto visceral fat of the subject.
 16. The apparatus of claim 15 whereinthe processing unit combines the plurality of regions in a linearequation using constants that provide correlation between DXA VAT andVAT measured by computed tomography.
 17. The apparatus of claim 15wherein the processing unit combines the plurality of regions usingpolynomial expansion.
 18. The apparatus of claim 15 wherein a firstregion and a second region are stored in memory, the first regionextending from a first side of the abdomen to a second side of theabdomen, and the second region extending across an inner abdominalcavity wall from the first side to the second side between innermostextents of an abdominal muscle wall.
 19. The apparatus of claim 15wherein a first region, a second region, and a third region are storedin memory, the first region extending from a first side of the abdomento a second side of the abdomen, the second region extending across aninner abdominal cavity from the first side to the second side betweeninnermost extents of an abdominal muscle wall but exclusive of a thirdregion which is placed where bone is present and percent fat cannot bedirectly measured.
 20. The apparatus of claim 18 wherein the processingunit places the first region of the image by computer-processing atleast some of the x-ray measurements.
 21. The apparatus of claim 20wherein the processing unit uses an anatomical landmark and apreselected region of interest line to place the first region of theimage.
 22. The apparatus of claim 18 wherein the processing unit placesthe second region of the image by computer-processing at least some ofthe x-ray measurements.
 23. The apparatus of claim 22 wherein theprocessing unit uses an algorithm to identify a left and a rightinnermost extent of abdominal muscle wall by identifying inflection ofadipose tissue values for placing the second region of the image. 24.The apparatus of claim 18 wherein the processing unit combines the firstregion and the second region using a linear equation which is correlatedwith VAT measured by quantitative computed tomography.
 25. The apparatusof claim 24 wherein the processing unit calculates visceral fat as:J*second region Mass−K*(first region Mass−second region Mass)+b.
 26. Theapparatus of claim 25 wherein constants J and K are selected based oncorrelation between DXA VAT and VAT measured by computed tomography, andwherein b is an intercept term.
 27. The apparatus of claim 25 wherein atleast one of J, K and b are selected for the subject.
 28. The apparatusof claim 27 wherein at least one of J, K and b are selected based on atleast one of age, gender, ethnicity, weight, height, body mass index,waist circumference, and other anthropomorphic variables of the subject.